Aims and Scope
Statistical Analysis and Data Mining addresses the broad area of data analysis, including data mining algorithms, statistical approaches, and practical applications. Topics include problems involving massive and complex datasets, solutions utilizing innovative data mining algorithms and/or novel statistical approaches, and the objective evaluation of analyses and solutions. Of special interest are articles that describe analytical techniques, and discuss their application to real problems, in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce.
The focus of the journal is on papers which satisfy one or more of the following criteria:
Solve data analysis problems associated with massive, complex datasets
Are application and solution oriented with a focus on solving real problems
Describe innovative data mining algorithms or novel statistical approaches
Compare and contrast techniques to solve a problem, along with an objective evaluation of the analyses and the solutions
The goals of this interdisciplinary journal are to encourage collaborations across disciplines, communication of novel data mining and statistical techniques to both novices and experts involved in the analysis of data from practical problems, and a principled evaluation of analyses and solutions.
The 21st Century has become a Century of Data, with most domains striving for useful general models for their mountains of data. Data mining and statistical analysis are amongst the most effective bodies of methodology and technology capable of producing useful general models from massive, complex datasets.
Statistical Analysis and Data Mining will be a useful resource to those solving practical problems, at the same time enabling them to benefit from ideas developed in other domains. It will be an international journal, with an interdisciplinary focus, covering areas which are becoming increasingly important, and likely to remain so in the foreseeable future.
Abstracting and Indexing Information
Computing Reviews (ACM)
Zentralblatt MATH/Mathematics Abstracts (FIZ Karlsruhe)
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